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Alzheimer’s disease (AD) is one of the most severe kinds of dementia that affects the elderly population. Since this disease is incurable and the changes in brain sub-regions start decades before the symptoms are observed, early detection becomes more challenging. Discriminating similar brain patterns for AD classification is difficult as minute ch...
Citations
... These two networks engage in an adversarial training process, where the generator endeavors to deceive the discriminator, while the discriminator strives to accurately identify the fabricated images. Pix2Pix is applied in various domains, including generating realistic images from sketches or labels [82], converting day-time images into night-time ones, and restoring missing parts of an image (MRI and PET images) [83,104]. ...
Generative models have emerged as potential tools for creating high-quality images, videos, and text. This paper explores the application of generative models in automating vlog content creation. It addresses both static and dynamic visual elements, eliminating the need for human intervention. Traditional vlogs often require specific environmental conditions and proper lighting for the vlog creation. To streamline this process, an automated system utilizing the generative models is proposed here. Generative models excel at generating realistic content that seamlessly integrates with real-world content. They enhance overall video quality and introduce creative elements by generating new scenes and backgrounds. This paper categorizes various generative modeling techniques based on frame elements and foreground-background conditions. It offers a comparative analysis of different generative model variants tailored for specific objectives. Furthermore, the paper reviews existing research on generative models for video and image content generation, visual quality enhancement, diversity, and coherence outcomes. Additionally, the paper highlights practical uses of the generative model for content creation in various contexts, such as face swapping, scene translation, and virtual content insertion. The paper also examines the public datasets used to train generative models. These datasets contain diverse visual content such as celebrity images, urban landscapes, and everyday scenes.
... Main cause of AD is high deposition of proteins in brain cells which is the main cause of damage in brain tissues & blocking the signal transmission from brain. The gradual deterioration of tissues results in the impairment of neuronal functions, leading to the degeneration and atrophy of different regions within the brain [8,10]. This atrophy in brain cells can be easily be detected by MRI (Magnetic resonance imaging). ...
... There are studies for automating the diagnosis of this disease. Conventional machine learning and deep learning-based approaches are proposed [16] to classify AD and their stages from different modalities of data. These Machine learning techniques specifically, deep learning techniques are gaining success in the early diagnosis of AD from magnetic resonance imaging (MRI) modality having better accuracy in binary classification while suffering in multiclass classification [2], [17]- [22]. ...
Alzheimer's disease (AD), a chronic neurodegener-ative brain disorder, caused by the accumulation of abnormal proteins called amyloid, is one of the prominent causes of mortality worldwide. Since there is a scarcity of experienced neurologists, manual diagnosis of AD is very time-consuming and error-prone. Hence, automatic diagnosis of AD draws significant attention nowadays. Machine learning (ML) algorithms such as deep learning are widely used to support early diagnosis of AD from magnetic resonance imaging (MRI). However, they provide better accuracy in binary classification, which is not the case with multi-class classification. On the other hand, AD consists of a number of early stages, and accurate detection of them is necessary. Hence, this research focuses on how to support the multi-stage classification of AD particularly in its early stage. After the MRI scans have been preprocessed (through median filtering and watershed segmentation), benchmark pre-trained convolutional neural network (CNN) models (AlexNet, VGG16, VGG19, ResNet18, ResNet50) carry out automatic feature extraction. Then, principal component analysis is used to optimize features. Conventional machine learning classifiers (Decision Tree, K-Nearest Neighbors, Support Vector Machine, Linear Programming Boost, and Total Boost) are deployed using the optimized features for staging AD. We have exploited the Alzheimer's disease Neuroimaging Initiative(ADNI) data set consisting of AD, MCIs (MCI), and cognitive normal (CN) classes of images. In our experiment, the SVM classifier performed better with the extracted ResNet50 features, achieving multi-class classification accuracy of 99.78% during training, 99.52% during validation, and 98.71% during testing. Our approach is distinctive because it combines the advantages of deep feature extractors, conventional classifiers, and feature optimization.
